Pandas Profiling Report

Dataset statistics

Number of variables	14
Number of observations	3687
Missing cells	9680
Missing cells (%)	18.8%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	403.4 KiB
Average record size in memory	112.0 B

Variable types

Numeric	7
Text	2
Boolean	2
Categorical	3

Alerts

`add_certificate` has constant value ""	Constant
`Id` is highly overall correlated with `price` and 5 other fields	High correlation
`price` is highly overall correlated with `Id` and 2 other fields	High correlation
`num_subscribers` is highly overall correlated with `Id` and 3 other fields	High correlation
`content_duration` is highly overall correlated with `Id` and 4 other fields	High correlation
`ingreso_por_curso` is highly overall correlated with `Id` and 3 other fields	High correlation
`price_Certificate` is highly overall correlated with `ingreso_por_curso` and 2 other fields	High correlation
`is_paid` is highly overall correlated with `Id` and 5 other fields	High correlation
`plataforma` is highly overall correlated with `Id` and 4 other fields	High correlation
`language` is highly imbalanced (80.3%)	Imbalance
`is_paid` has 491 (13.3%) missing values	Missing
`price` has 491 (13.3%) missing values	Missing
`level` has 287 (7.8%) missing values	Missing
`content_duration` has 491 (13.3%) missing values	Missing
`rating` has 867 (23.5%) missing values	Missing
`language` has 448 (12.2%) missing values	Missing
`category` has 448 (12.2%) missing values	Missing
`ingreso_por_curso` has 491 (13.3%) missing values	Missing
`add_certificate` has 2833 (76.8%) missing values	Missing
`price_Certificate` has 2833 (76.8%) missing values	Missing
`num_subscribers` is highly skewed (γ1 = 38.93832754)	Skewed
`Id` is uniformly distributed	Uniform
`Id` has unique values	Unique
`price` has 1070 (29.0%) zeros	Zeros
`ingreso_por_curso` has 236 (6.4%) zeros	Zeros

Reproduction

Analysis started	2023-06-17 01:48:02.861906
Analysis finished	2023-06-17 01:48:17.023082
Duration	14.16 seconds
Software version	ydata-profiling vv4.2.0
Download configuration	config.json

Id
Real number (ℝ)

HIGH CORRELATION UNIFORM UNIQUE

Distinct	3687
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1844

Minimum	1
Maximum	3687
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	28.9 KiB

Quantile statistics

Minimum	1
5-th percentile	185.3
Q1	922.5
median	1844
Q3	2765.5
95-th percentile	3502.7
Maximum	3687
Range	3686
Interquartile range (IQR)	1843

Descriptive statistics

Standard deviation	1064.4895
Coefficient of variation (CV)	0.57727199
Kurtosis	-1.2
Mean	1844
Median Absolute Deviation (MAD)	922
Skewness	0
Sum	6798828
Variance	1133138
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	< 0.1%
2450	1	< 0.1%
2452	1	< 0.1%
2453	1	< 0.1%
2454	1	< 0.1%
2455	1	< 0.1%
2456	1	< 0.1%
2457	1	< 0.1%
2458	1	< 0.1%
2459	1	< 0.1%
Other values (3677)	3677	99.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	< 0.1%
2	1	< 0.1%
3	1	< 0.1%
4	1	< 0.1%
5	1	< 0.1%
6	1	< 0.1%
7	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%
10	1	< 0.1%

Value	Count	Frequency (%)
3687	1	< 0.1%
3686	1	< 0.1%
3685	1	< 0.1%
3684	1	< 0.1%
3683	1	< 0.1%
3682	1	< 0.1%
3681	1	< 0.1%
3680	1	< 0.1%
3679	1	< 0.1%
3678	1	< 0.1%

course_title
Text

Distinct	3652
Distinct (%)	99.1%
Missing	0
Missing (%)	0.0%
Memory size	28.9 KiB

Length

Max length	243
Median length	84
Mean length	43.252509
Min length	7

Characters and Unicode

Total characters	159472
Distinct characters	466
Distinct categories	16 ?
Distinct scripts	8 ?
Distinct blocks	12 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3624 ?
Unique (%)	98.3%

Sample

1st row	Ultimate Investment Banking Course
2nd row	Complete GST Course & Certification - Grow Your CA Practice
3rd row	Financial Modeling for Business Analysts and Consultants
4th row	Beginner to Pro - Financial Analysis in Excel 2017
5th row	How To Maximize Your Profits Trading Options

Value	Count	Frequency (%)
	786	3.3%
to	669	2.8%
and	661	2.8%
for	543	2.3%
the	494	2.1%
a	362	1.5%
learn	327	1.4%
in	325	1.4%
with	298	1.2%
of	228	1.0%
Other values (4377)	19202	80.4%

Most occurring characters

Value	Count	Frequency (%)
	20290	12.7%
e	12782	8.0%
n	10735	6.7%
i	9981	6.3%
a	9922	6.2%
o	9678	6.1%
t	8980	5.6%
r	8768	5.5%
s	7387	4.6%
l	4683	2.9%
Other values (456)	56266	35.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	113753	71.3%
Space Separator	20309	12.7%
Uppercase Letter	19839	12.4%
Other Punctuation	2133	1.3%
Decimal Number	1394	0.9%
Other Letter	983	0.6%
Dash Punctuation	675	0.4%
Close Punctuation	120	0.1%
Open Punctuation	120	0.1%
Math Symbol	75	< 0.1%
Other values (6)	71	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ل	46	4.7%
ا	41	4.2%
و	24	2.4%
ي	19	1.9%
ス	19	1.9%
で	19	1.9%
م	19	1.9%
ン	17	1.7%
の	17	1.7%
ر	16	1.6%
Other values (281)	746	75.9%

Lowercase Letter

Value	Count	Frequency (%)
e	12782	11.2%
n	10735	9.4%
i	9981	8.8%
a	9922	8.7%
o	9678	8.5%
t	8980	7.9%
r	8768	7.7%
s	7387	6.5%
l	4683	4.1%
c	4401	3.9%
Other values (61)	26436	23.2%

Uppercase Letter

Value	Count	Frequency (%)
S	1877	9.5%
C	1741	8.8%
P	1680	8.5%
T	1460	7.4%
A	1332	6.7%
B	1232	6.2%
M	1120	5.6%
F	1037	5.2%
L	1037	5.2%
D	1016	5.1%
Other values (27)	6307	31.8%

Other Punctuation

Value	Count	Frequency (%)
:	989	46.4%
,	340	15.9%
&	230	10.8%
.	167	7.8%
!	146	6.8%
'	108	5.1%
/	61	2.9%
#	25	1.2%
"	20	0.9%
?	16	0.8%
Other values (8)	31	1.5%

Decimal Number

Value	Count	Frequency (%)
1	363	26.0%
2	238	17.1%
0	222	15.9%
5	150	10.8%
3	136	9.8%
4	98	7.0%
6	60	4.3%
7	55	3.9%
8	36	2.6%
9	31	2.2%
Other values (3)	5	0.4%

Math Symbol

Value	Count	Frequency (%)
+	36	48.0%
\|	33	44.0%
>	2	2.7%
｜	1	1.3%
≪	1	1.3%
≫	1	1.3%
=	1	1.3%

Close Punctuation

Value	Count	Frequency (%)
)	109	90.8%
」	3	2.5%
）	3	2.5%
]	2	1.7%
】	2	1.7%
}	1	0.8%

Open Punctuation

Value	Count	Frequency (%)
(	109	90.8%
「	3	2.5%
（	3	2.5%
[	2	1.7%
【	2	1.7%
{	1	0.8%

Dash Punctuation

Value	Count	Frequency (%)
-	648	96.0%
–	24	3.6%
〜	2	0.3%
—	1	0.1%

Space Separator

Value	Count	Frequency (%)
	20290	99.9%
	12	0.1%
	7	< 0.1%

Letter Number

Value	Count	Frequency (%)
Ⅰ	2	50.0%
Ⅱ	1	25.0%
Ⅲ	1	25.0%

Other Symbol

Value	Count	Frequency (%)
®	12	85.7%
™	2	14.3%

Modifier Symbol

Value	Count	Frequency (%)
`	1	50.0%
´	1	50.0%

Final Punctuation

Value	Count	Frequency (%)
’	24	100.0%

Modifier Letter

Value	Count	Frequency (%)
ー	23	100.0%

Control

Value	Count	Frequency (%)
	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	133378	83.6%
Common	24893	15.6%
Han	318	0.2%
Arabic	285	0.2%
Cyrillic	218	0.1%
Katakana	192	0.1%
Hiragana	179	0.1%
Hangul	9	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
学	14	4.4%
資	7	2.2%
日	7	2.2%
入	6	1.9%
基	6	1.9%
投	5	1.6%
礎	5	1.6%
分	5	1.6%
生	5	1.6%
式	5	1.6%
Other values (156)	253	79.6%

Latin

Value	Count	Frequency (%)
e	12782	9.6%
n	10735	8.0%
i	9981	7.5%
a	9922	7.4%
o	9678	7.3%
t	8980	6.7%
r	8768	6.6%
s	7387	5.5%
l	4683	3.5%
c	4401	3.3%
Other values (68)	46061	34.5%

Common

Value	Count	Frequency (%)
	20290	81.5%
:	989	4.0%
-	648	2.6%
1	363	1.5%
,	340	1.4%
2	238	1.0%
&	230	0.9%
0	222	0.9%
.	167	0.7%
5	150	0.6%
Other values (54)	1256	5.0%

Katakana

Value	Count	Frequency (%)
ス	19	9.9%
ン	17	8.9%
タ	14	7.3%
ト	12	6.2%
ッ	10	5.2%
ギ	10	5.2%
レ	8	4.2%
ル	7	3.6%
リ	7	3.6%
イ	6	3.1%
Other values (36)	82	42.7%

Hiragana

Value	Count	Frequency (%)
で	19	10.6%
の	17	9.5%
る	15	8.4%
を	12	6.7%
に	11	6.1%
う	7	3.9%
き	7	3.9%
な	7	3.9%
ぶ	5	2.8%
も	5	2.8%
Other values (32)	74	41.3%

Cyrillic

Value	Count	Frequency (%)
а	28	12.8%
н	20	9.2%
о	19	8.7%
и	19	8.7%
р	14	6.4%
т	13	6.0%
е	12	5.5%
в	10	4.6%
л	10	4.6%
м	8	3.7%
Other values (23)	65	29.8%

Arabic

Value	Count	Frequency (%)
ل	46	16.1%
ا	41	14.4%
و	24	8.4%
ي	19	6.7%
م	19	6.7%
ر	16	5.6%
ت	14	4.9%
س	14	4.9%
د	10	3.5%
ة	10	3.5%
Other values (18)	72	25.3%

Hangul

Value	Count	Frequency (%)
만	1	11.1%
캔	1	11.1%
바	1	11.1%
로	1	11.1%
콘	1	11.1%
텐	1	11.1%
츠	1	11.1%
들	1	11.1%
기	1	11.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	157736	98.9%
None	455	0.3%
CJK	318	0.2%
Arabic	285	0.2%
Cyrillic	218	0.1%
Katakana	215	0.1%
Hiragana	179	0.1%
Punctuation	49	< 0.1%
Hangul	9	< 0.1%
Number Forms	4	< 0.1%
Other values (2)	4	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	20290	12.9%
e	12782	8.1%
n	10735	6.8%
i	9981	6.3%
a	9922	6.3%
o	9678	6.1%
t	8980	5.7%
r	8768	5.6%
s	7387	4.7%
l	4683	3.0%
Other values (79)	54530	34.6%

None

Value	Count	Frequency (%)
ó	141	31.0%
á	51	11.2%
í	49	10.8%
é	38	8.4%
ñ	24	5.3%
ã	16	3.5%
ç	15	3.3%
	12	2.6%
®	12	2.6%
ú	12	2.6%
Other values (33)	85	18.7%

Arabic

Value	Count	Frequency (%)
ل	46	16.1%
ا	41	14.4%
و	24	8.4%
ي	19	6.7%
م	19	6.7%
ر	16	5.6%
ت	14	4.9%
س	14	4.9%
د	10	3.5%
ة	10	3.5%
Other values (18)	72	25.3%

Cyrillic

Value	Count	Frequency (%)
а	28	12.8%
н	20	9.2%
о	19	8.7%
и	19	8.7%
р	14	6.4%
т	13	6.0%
е	12	5.5%
в	10	4.6%
л	10	4.6%
м	8	3.7%
Other values (23)	65	29.8%

Punctuation

Value	Count	Frequency (%)
–	24	49.0%
’	24	49.0%
—	1	2.0%

Katakana

Value	Count	Frequency (%)
ー	23	10.7%
ス	19	8.8%
ン	17	7.9%
タ	14	6.5%
ト	12	5.6%
ッ	10	4.7%
ギ	10	4.7%
レ	8	3.7%
ル	7	3.3%
リ	7	3.3%
Other values (37)	88	40.9%

Hiragana

Value	Count	Frequency (%)
で	19	10.6%
の	17	9.5%
る	15	8.4%
を	12	6.7%
に	11	6.1%
う	7	3.9%
き	7	3.9%
な	7	3.9%
ぶ	5	2.8%
も	5	2.8%
Other values (32)	74	41.3%

CJK

Value	Count	Frequency (%)
学	14	4.4%
資	7	2.2%
日	7	2.2%
入	6	1.9%
基	6	1.9%
投	5	1.6%
礎	5	1.6%
分	5	1.6%
生	5	1.6%
式	5	1.6%
Other values (156)	253	79.6%

Letterlike Symbols

Value	Count	Frequency (%)
™	2	100.0%

Number Forms

Value	Count	Frequency (%)
Ⅰ	2	50.0%
Ⅱ	1	25.0%
Ⅲ	1	25.0%

Math Operators

Value	Count	Frequency (%)
≪	1	50.0%
≫	1	50.0%

Hangul

Value	Count	Frequency (%)
만	1	11.1%
캔	1	11.1%
바	1	11.1%
로	1	11.1%
콘	1	11.1%
텐	1	11.1%
츠	1	11.1%
들	1	11.1%
기	1	11.1%

is_paid
Boolean

HIGH CORRELATION MISSING

Distinct	2
Distinct (%)	0.1%
Missing	491
Missing (%)	13.3%
Memory size	28.9 KiB

True	2126
False	1070
(Missing)	491

Common Values (Table)
Common Values (Plot)

Value	Count	Frequency (%)
True	2126	57.7%
False	1070	29.0%
(Missing)	491	13.3%

price
Real number (ℝ)

HIGH CORRELATION MISSING ZEROS

Distinct	38
Distinct (%)	1.2%
Missing	491
Missing (%)	13.3%
Infinite	0
Infinite (%)	0.0%
Mean	53.110138

Minimum	0
Maximum	200
Zeros	1070
Zeros (%)	29.0%
Negative	0
Negative (%)	0.0%
Memory size	28.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	30
Q3	80
95-th percentile	200
Maximum	200
Range	200
Interquartile range (IQR)	80

Descriptive statistics

Standard deviation	63.108782
Coefficient of variation (CV)	1.1882624
Kurtosis	0.37585896
Mean	53.110138
Median Absolute Deviation (MAD)	30
Skewness	1.2496335
Sum	169740
Variance	3982.7184
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=38)

Value	Count	Frequency (%)
0	1070	29.0%
20	396	10.7%
50	256	6.9%
200	212	5.7%
40	148	4.0%
30	113	3.1%
95	105	2.8%
100	105	2.8%
195	101	2.7%
25	86	2.3%
Other values (28)	604	16.4%
(Missing)	491	13.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1070	29.0%
20	396	10.7%
25	86	2.3%
30	113	3.1%
35	67	1.8%
40	148	4.0%
45	64	1.7%
50	256	6.9%
55	24	0.7%
60	62	1.7%

Value	Count	Frequency (%)
200	212	5.7%
195	101	2.7%
190	8	0.2%
185	5	0.1%
180	14	0.4%
175	10	0.3%
170	2	0.1%
165	5	0.1%
160	3	0.1%
155	1	< 0.1%

num_subscribers
Real number (ℝ)

HIGH CORRELATION SKEWED

Distinct	2630
Distinct (%)	71.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	46260.841

Minimum	0
Maximum	32000000
Zeros	20
Zeros (%)	0.5%
Negative	0
Negative (%)	0.0%
Memory size	28.9 KiB

Quantile statistics

Minimum	0
5-th percentile	23
Q1	652.5
median	4005
Q3	22984
95-th percentile	120013.3
Maximum	32000000
Range	32000000
Interquartile range (IQR)	22331.5

Descriptive statistics

Standard deviation	705431.65
Coefficient of variation (CV)	15.249002
Kurtosis	1597.3903
Mean	46260.841
Median Absolute Deviation (MAD)	3947
Skewness	38.938328
Sum	1.7056372 × 10⁸
Variance	4.9763381 × 10¹¹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	20	0.5%
120000	15	0.4%
130000	13	0.4%
7	13	0.4%
1	13	0.4%
11	12	0.3%
2	12	0.3%
12000	11	0.3%
110000	11	0.3%
5	10	0.3%
Other values (2620)	3557	96.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	20	0.5%
1	13	0.4%
2	12	0.3%
3	7	0.2%
4	7	0.2%
5	10	0.3%
6	5	0.1%
7	13	0.4%
8	6	0.2%
9	10	0.3%

Value	Count	Frequency (%)
32000000	1	< 0.1%
25000000	1	< 0.1%
13000000	1	< 0.1%
2442271	1	< 0.1%
1103777	1	< 0.1%
1022489	1	< 0.1%
760000	1	< 0.1%
750000	1	< 0.1%
698950	1	< 0.1%
642088	1	< 0.1%

level
Categorical

Distinct	4
Distinct (%)	0.1%
Missing	287
Missing (%)	7.8%
Memory size	28.9 KiB

Beginner	1460
All	1291
Intermediate	529
Expert	120

Length

Max length	12
Median length	8
Mean length	6.6532353
Min length	3

Characters and Unicode

Total characters	22621
Distinct characters	16
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	All
2nd row	All
3rd row	Intermediate
4th row	All
5th row	Intermediate

Common Values

Value	Count	Frequency (%)
Beginner	1460	39.6%
All	1291	35.0%
Intermediate	529	14.3%
Expert	120	3.3%
(Missing)	287	7.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
beginner	1460	42.9%
all	1291	38.0%
intermediate	529	15.6%
expert	120	3.5%

Most occurring characters

Value	Count	Frequency (%)
e	4627	20.5%
n	3449	15.2%
l	2582	11.4%
r	2109	9.3%
i	1989	8.8%
B	1460	6.5%
g	1460	6.5%
A	1291	5.7%
t	1178	5.2%
I	529	2.3%
Other values (6)	1947	8.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	19221	85.0%
Uppercase Letter	3400	15.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	4627	24.1%
n	3449	17.9%
l	2582	13.4%
r	2109	11.0%
i	1989	10.3%
g	1460	7.6%
t	1178	6.1%
m	529	2.8%
d	529	2.8%
a	529	2.8%
Other values (2)	240	1.2%

Uppercase Letter

Value	Count	Frequency (%)
B	1460	42.9%
A	1291	38.0%
I	529	15.6%
E	120	3.5%

Most occurring scripts

Value	Count	Frequency (%)
Latin	22621	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	4627	20.5%
n	3449	15.2%
l	2582	11.4%
r	2109	9.3%
i	1989	8.8%
B	1460	6.5%
g	1460	6.5%
A	1291	5.7%
t	1178	5.2%
I	529	2.3%
Other values (6)	1947	8.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	22621	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	4627	20.5%
n	3449	15.2%
l	2582	11.4%
r	2109	9.3%
i	1989	8.8%
B	1460	6.5%
g	1460	6.5%
A	1291	5.7%
t	1178	5.2%
I	529	2.3%
Other values (6)	1947	8.6%

content_duration
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	115
Distinct (%)	3.6%
Missing	491
Missing (%)	13.3%
Infinite	0
Infinite (%)	0.0%
Mean	310.8531

Minimum	0.31666667
Maximum	3024
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	28.9 KiB

Quantile statistics

Minimum	0.31666667
5-th percentile	1
Q1	1.5
median	4
Q3	672
95-th percentile	1680
Maximum	3024
Range	3023.6833
Interquartile range (IQR)	670.5

Descriptive statistics

Standard deviation	576.2475
Coefficient of variation (CV)	1.8537614
Kurtosis	3.2497266
Mean	310.8531
Median Absolute Deviation (MAD)	3
Skewness	1.9340923
Sum	993486.52
Variance	332061.18
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1.5	331	9.0%
1	325	8.8%
2	259	7.0%
2.5	180	4.9%
1008	173	4.7%
3	162	4.4%
672	159	4.3%
3.5	121	3.3%
840	119	3.2%
4	98	2.7%
Other values (105)	1269	34.4%
(Missing)	491	13.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.3166666667	1	< 0.1%
0.4666666667	1	< 0.1%
0.5	9	0.2%
0.5166666667	6	0.2%
0.5333333333	13	0.4%
0.55	8	0.2%
0.5666666667	11	0.3%
0.5833333333	6	0.2%
0.6	8	0.2%
0.6166666667	16	0.4%

Value	Count	Frequency (%)
3024	3	0.1%
2856	1	< 0.1%
2688	12	0.3%
2520	18	0.5%
2352	15	0.4%
2184	6	0.2%
2016	34	0.9%
1848	6	0.2%
1680	79	2.1%
1512	20	0.5%

rating
Real number (ℝ)

Distinct	30
Distinct (%)	1.1%
Missing	867
Missing (%)	23.5%
Infinite	0
Infinite (%)	0.0%
Mean	4.2868794

Minimum	0
Maximum	5
Zeros	4
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	28.9 KiB

Quantile statistics

Minimum	0
5-th percentile	3.4
Q1	4
median	4.4
Q3	4.6
95-th percentile	4.8
Maximum	5
Range	5
Interquartile range (IQR)	0.6

Descriptive statistics

Standard deviation	0.495934
Coefficient of variation (CV)	0.11568648
Kurtosis	11.535028
Mean	4.2868794
Median Absolute Deviation (MAD)	0.3
Skewness	-2.2072273
Sum	12089
Variance	0.24595053
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=30)

Value	Count	Frequency (%)
4.4	376	10.2%
4.6	341	9.2%
4.8	276	7.5%
4.2	272	7.4%
4.7	215	5.8%
4	201	5.5%
4.5	200	5.4%
4.3	163	4.4%
3.8	143	3.9%
4.1	104	2.8%
Other values (20)	529	14.3%
(Missing)	867	23.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	4	0.1%
1	5	0.1%
2	4	0.1%
2.2	3	0.1%
2.5	3	0.1%
2.6	4	0.1%
2.7	1	< 0.1%
2.8	11	0.3%
2.9	1	< 0.1%
3	18	0.5%

Value	Count	Frequency (%)
5	37	1.0%
4.9	99	2.7%
4.8	276	7.5%
4.7	215	5.8%
4.6	341	9.2%
4.5	200	5.4%
4.4	376	10.2%
4.3	163	4.4%
4.2	272	7.4%
4.1	104	2.8%

language
Categorical

IMBALANCE MISSING

Distinct	14
Distinct (%)	0.4%
Missing	448
Missing (%)	12.2%
Memory size	28.9 KiB

English	2872
Spanish	220
Portuguese	46
Japanese	29
German	19
Other values (9)	53

Length

Max length	10
Median length	7
Mean length	7.0342698
Min length	4

Characters and Unicode

Total characters	22784
Distinct characters	32
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.1%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	2872	77.9%
Spanish	220	6.0%
Portuguese	46	1.2%
Japanese	29	0.8%
German	19	0.5%
French	17	0.5%
Arabic	12	0.3%
Italian	8	0.2%
Russian	4	0.1%
Chinese	4	0.1%
Other values (4)	8	0.2%
(Missing)	448	12.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	2872	88.7%
spanish	220	6.8%
portuguese	46	1.4%
japanese	29	0.9%
german	19	0.6%
french	17	0.5%
arabic	12	0.4%
italian	8	0.2%
russian	4	0.1%
chinese	4	0.1%
Other values (4)	8	0.2%

Most occurring characters

Value	Count	Frequency (%)
s	3183	14.0%
n	3176	13.9%
i	3128	13.7%
h	3117	13.7%
g	2918	12.8%
l	2880	12.6%
E	2872	12.6%
a	330	1.4%
p	249	1.1%
S	220	1.0%
Other values (22)	711	3.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	19545	85.8%
Uppercase Letter	3239	14.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
s	3183	16.3%
n	3176	16.2%
i	3128	16.0%
h	3117	15.9%
g	2918	14.9%
l	2880	14.7%
a	330	1.7%
p	249	1.3%
e	195	1.0%
u	101	0.5%
Other values (8)	268	1.4%

Uppercase Letter

Value	Count	Frequency (%)
E	2872	88.7%
S	220	6.8%
P	46	1.4%
J	29	0.9%
G	19	0.6%
F	17	0.5%
A	12	0.4%
I	8	0.2%
R	4	0.1%
C	4	0.1%
Other values (4)	8	0.2%

Most occurring scripts

Value	Count	Frequency (%)
Latin	22784	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
s	3183	14.0%
n	3176	13.9%
i	3128	13.7%
h	3117	13.7%
g	2918	12.8%
l	2880	12.6%
E	2872	12.6%
a	330	1.4%
p	249	1.1%
S	220	1.0%
Other values (22)	711	3.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	22784	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
s	3183	14.0%
n	3176	13.9%
i	3128	13.7%
h	3117	13.7%
g	2918	12.8%
l	2880	12.6%
E	2872	12.6%
a	330	1.4%
p	249	1.1%
S	220	1.0%
Other values (22)	711	3.1%

category
Text

Distinct	54
Distinct (%)	1.7%
Missing	448
Missing (%)	12.2%
Memory size	28.9 KiB

Length

Max length	28
Median length	26
Mean length	13.103736
Min length	3

Characters and Unicode

Total characters	42443
Distinct characters	42
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7 ?
Unique (%)	0.2%

Sample

1st row	Finance & Accounting
2nd row	Finance & Accounting
3rd row	Finance & Accounting
4th row	Finance & Accounting
5th row	Finance & Accounting

Value	Count	Frequency (%)
	1070	18.5%
development	852	14.8%
finance	763	13.2%
accounting	726	12.6%
music	407	7.1%
design	363	6.3%
science	159	2.8%
business	156	2.7%
computer	150	2.6%
management	149	2.6%
Other values (63)	977	16.9%

Most occurring characters

Value	Count	Frequency (%)
n	5500	13.0%
e	5234	12.3%
i	3561	8.4%
c	3469	8.2%
	2533	6.0%
t	2446	5.8%
o	2133	5.0%
s	1814	4.3%
a	1706	4.0%
u	1647	3.9%
Other values (32)	12400	29.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	34153	80.5%
Uppercase Letter	4687	11.0%
Space Separator	2533	6.0%
Other Punctuation	1070	2.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	5500	16.1%
e	5234	15.3%
i	3561	10.4%
c	3469	10.2%
t	2446	7.2%
o	2133	6.2%
s	1814	5.3%
a	1706	5.0%
u	1647	4.8%
g	1467	4.3%
Other values (12)	5176	15.2%

Uppercase Letter

Value	Count	Frequency (%)
D	1279	27.3%
A	813	17.3%
F	769	16.4%
M	609	13.0%
S	381	8.1%
C	202	4.3%
B	190	4.1%
E	159	3.4%
H	88	1.9%
L	81	1.7%
Other values (8)	116	2.5%

Space Separator

Value	Count	Frequency (%)
	2533	100.0%

Other Punctuation

Value	Count	Frequency (%)
&	1070	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	38840	91.5%
Common	3603	8.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	5500	14.2%
e	5234	13.5%
i	3561	9.2%
c	3469	8.9%
t	2446	6.3%
o	2133	5.5%
s	1814	4.7%
a	1706	4.4%
u	1647	4.2%
g	1467	3.8%
Other values (30)	9863	25.4%

Common

Value	Count	Frequency (%)
	2533	70.3%
&	1070	29.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	42443	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	5500	13.0%
e	5234	12.3%
i	3561	8.4%
c	3469	8.2%
	2533	6.0%
t	2446	5.8%
o	2133	5.0%
s	1814	4.3%
a	1706	4.0%
u	1647	3.9%
Other values (32)	12400	29.2%

ingreso_por_curso
Real number (ℝ)

HIGH CORRELATION MISSING ZEROS

Distinct	2664
Distinct (%)	83.4%
Missing	491
Missing (%)	13.3%
Infinite	0
Infinite (%)	0.0%
Mean	1783000.8

Minimum	0
Maximum	2.1980439 × 10⁸
Zeros	236
Zeros (%)	6.4%
Negative	0
Negative (%)	0.0%
Memory size	28.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	9642.5
median	108835
Q3	882197.75
95-th percentile	7941362
Maximum	2.1980439 × 10⁸
Range	2.1980439 × 10⁸
Interquartile range (IQR)	872555.25

Descriptive statistics

Standard deviation	7425632.3
Coefficient of variation (CV)	4.1646825
Kurtosis	298.61903
Mean	1783000.8
Median Absolute Deviation (MAD)	108775
Skewness	13.880577
Sum	5.6984706 × 10⁹
Variance	5.5140014 × 10¹³
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	236	6.4%
200	8	0.2%
900	7	0.2%
760	5	0.1%
60	5	0.1%
1000	5	0.1%
540	5	0.1%
350	5	0.1%
600	5	0.1%
1500	5	0.1%
Other values (2654)	2910	78.9%
(Missing)	491	13.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	236	6.4%
20	2	0.1%
30	4	0.1%
35	1	< 0.1%
40	3	0.1%
50	2	0.1%
60	5	0.1%
65	1	< 0.1%
70	2	0.1%
75	1	< 0.1%

Value	Count	Frequency (%)
219804390	1	< 0.1%
127775512	1	< 0.1%
109273923	1	< 0.1%
93442300	1	< 0.1%
80378766	1	< 0.1%
78331914	1	< 0.1%
66843105	1	< 0.1%
66010896	1	< 0.1%
62905500	1	< 0.1%
61684500	1	< 0.1%

plataforma
Categorical

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	28.9 KiB

Udemy	2342
edX	854
Coursera	491

Length

Max length	8
Median length	5
Mean length	4.9362625
Min length	3

Characters and Unicode

Total characters	18200
Distinct characters	12
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Udemy
2nd row	Udemy
3rd row	Udemy
4th row	Udemy
5th row	Udemy

Common Values

Value	Count	Frequency (%)
Udemy	2342	63.5%
edX	854	23.2%
Coursera	491	13.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
udemy	2342	63.5%
edx	854	23.2%
coursera	491	13.3%

Most occurring characters

Value	Count	Frequency (%)
e	3687	20.3%
d	3196	17.6%
U	2342	12.9%
m	2342	12.9%
y	2342	12.9%
r	982	5.4%
X	854	4.7%
C	491	2.7%
o	491	2.7%
u	491	2.7%
Other values (2)	982	5.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	14513	79.7%
Uppercase Letter	3687	20.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3687	25.4%
d	3196	22.0%
m	2342	16.1%
y	2342	16.1%
r	982	6.8%
o	491	3.4%
u	491	3.4%
s	491	3.4%
a	491	3.4%

Uppercase Letter

Value	Count	Frequency (%)
U	2342	63.5%
X	854	23.2%
C	491	13.3%

Most occurring scripts

Value	Count	Frequency (%)
Latin	18200	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3687	20.3%
d	3196	17.6%
U	2342	12.9%
m	2342	12.9%
y	2342	12.9%
r	982	5.4%
X	854	4.7%
C	491	2.7%
o	491	2.7%
u	491	2.7%
Other values (2)	982	5.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	18200	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	3687	20.3%
d	3196	17.6%
U	2342	12.9%
m	2342	12.9%
y	2342	12.9%
r	982	5.4%
X	854	4.7%
C	491	2.7%
o	491	2.7%
u	491	2.7%
Other values (2)	982	5.4%

add_certificate
Boolean

CONSTANT MISSING

Distinct	1
Distinct (%)	0.1%
Missing	2833
Missing (%)	76.8%
Memory size	28.9 KiB

True	854
(Missing)	2833

Common Values (Table)
Common Values (Plot)

Value	Count	Frequency (%)
True	854	23.2%
(Missing)	2833	76.8%

price_Certificate
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	41
Distinct (%)	4.8%
Missing	2833
Missing (%)	76.8%
Infinite	0
Infinite (%)	0.0%
Mean	101.64286

Minimum	5
Maximum	450
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	28.9 KiB

Quantile statistics

Minimum	5
5-th percentile	25
Q1	49
median	79
Q3	149
95-th percentile	249
Maximum	450
Range	445
Interquartile range (IQR)	100

Descriptive statistics

Standard deviation	70.640447
Coefficient of variation (CV)	0.69498683
Kurtosis	1.8183625
Mean	101.64286
Median Absolute Deviation (MAD)	30
Skewness	1.2949606
Sum	86803
Variance	4990.0728
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=41)

Value	Count	Frequency (%)
49	240	6.5%
99	126	3.4%
50	83	2.3%
199	73	2.0%
149	62	1.7%
25	43	1.2%
139	31	0.8%
150	29	0.8%
249	25	0.7%
169	19	0.5%
Other values (31)	123	3.3%
(Missing)	2833	76.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
5	7	0.2%
10	1	< 0.1%
15	1	< 0.1%
19	2	0.1%
25	43	1.2%
29	11	0.3%
39	9	0.2%
40	2	0.1%
49	240	6.5%
50	83	2.3%

Value	Count	Frequency (%)
450	1	< 0.1%
399	3	0.1%
375	1	< 0.1%
350	3	0.1%
300	3	0.1%
299	10	0.3%
250	2	0.1%
249	25	0.7%
225	3	0.1%
214	3	0.1%

Auto

Heatmap
Table

	Id	price	num_subscribers	content_duration	rating	ingreso_por_curso	price_Certificate	is_paid	level	language	plataforma
Id	1.000	-0.555	0.730	0.644	0.289	0.582	0.000	0.795	0.266	0.130	0.894
price	-0.555	1.000	-0.486	-0.391	0.016	-0.139	NaN	0.769	0.224	0.071	0.654
num_subscribers	0.730	-0.486	1.000	0.610	0.264	0.802	0.138	1.000	0.000	0.000	0.043
content_duration	0.644	-0.391	0.610	1.000	0.133	0.682	0.094	0.845	0.301	0.126	0.993
rating	0.289	0.016	0.264	0.133	1.000	-0.015	NaN	0.087	0.078	0.262	0.421
ingreso_por_curso	0.582	-0.139	0.802	0.682	-0.015	1.000	0.625	0.151	0.074	0.000	0.181
price_Certificate	0.000	NaN	0.138	0.094	NaN	0.625	1.000	1.000	0.329	0.128	1.000
is_paid	0.795	0.769	1.000	0.845	0.087	0.151	1.000	1.000	0.452	0.223	0.850
level	0.266	0.224	0.000	0.301	0.078	0.074	0.329	0.452	1.000	0.094	0.339
language	0.130	0.071	0.000	0.126	0.262	0.000	0.128	0.223	0.094	1.000	0.172
plataforma	0.894	0.654	0.043	0.993	0.421	0.181	1.000	0.850	0.339	0.172	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	Id	course_title	is_paid	price	num_subscribers	level	content_duration	rating	language	category	ingreso_por_curso	plataforma	add_certificate	price_Certificate
0	1	Ultimate Investment Banking Course	True	200.0	2147.0	All	1.5	3.6	English	Finance & Accounting	429400.0	Udemy	NaN	NaN
1	2	Complete GST Course & Certification - Grow Your CA Practice	True	75.0	2792.0	All	39.0	4.4	English	Finance & Accounting	209400.0	Udemy	NaN	NaN
2	3	Financial Modeling for Business Analysts and Consultants	True	45.0	2174.0	Intermediate	2.5	4.4	English	Finance & Accounting	97830.0	Udemy	NaN	NaN
3	4	Beginner to Pro - Financial Analysis in Excel 2017	True	95.0	2451.0	All	3.0	4.2	English	Finance & Accounting	232845.0	Udemy	NaN	NaN
4	5	How To Maximize Your Profits Trading Options	True	200.0	1276.0	Intermediate	2.0	4.7	English	Finance & Accounting	255200.0	Udemy	NaN	NaN
5	6	Investing And Trading For Beginners: Mastering Price Charts	True	65.0	1540.0	Beginner	1.0	4.6	English	Finance & Accounting	100100.0	Udemy	NaN	NaN
6	7	Trading Stock Chart Patterns For Immediate, Explosive Gains	True	95.0	2917.0	All	2.5	4.2	English	Finance & Accounting	277115.0	Udemy	NaN	NaN
7	8	The Only Investment Strategy You Need For Your Retirement	True	200.0	827.0	All	1.0	3.9	English	Finance & Accounting	165400.0	Udemy	NaN	NaN
8	9	Forex Trading Secrets of the Pros With Amazon's AWS	True	200.0	4284.0	All	5.0	3.8	English	Finance & Accounting	856800.0	Udemy	NaN	NaN
9	10	Trading Options With Money Flow	True	200.0	1380.0	All	1.0	4.3	English	Finance & Accounting	276000.0	Udemy	NaN	NaN

	Id	course_title	is_paid	price	num_subscribers	level	content_duration	rating	language	category	ingreso_por_curso	plataforma	add_certificate	price_Certificate
3677	3678	Mastering Final Cut Pro	NaN	NaN	84000.0	NaN	NaN	4.5	NaN	NaN	NaN	Coursera	NaN	NaN
3678	3679	Tricky American English Pronunciation	NaN	NaN	110000.0	NaN	NaN	4.7	NaN	NaN	NaN	Coursera	NaN	NaN
3679	3680	International Law in Action: A Guide to the International Courts and Tribunals in The Hague	NaN	NaN	36000.0	All	NaN	4.8	NaN	NaN	NaN	Coursera	NaN	NaN
3680	3681	Geopolitics of Europe	NaN	NaN	19000.0	All	NaN	3.7	NaN	NaN	NaN	Coursera	NaN	NaN
3681	3682	Osteoarchaeology: The Truth in Our Bones	NaN	NaN	22000.0	NaN	NaN	4.6	NaN	NaN	NaN	Coursera	NaN	NaN
3682	3683	Mathematics for Machine Learning: PCA	NaN	NaN	33000.0	NaN	NaN	3.4	NaN	NaN	NaN	Coursera	NaN	NaN
3683	3684	Object Oriented Programming in Java	NaN	NaN	330000.0	NaN	NaN	4.7	NaN	NaN	NaN	Coursera	NaN	NaN
3684	3685	Математика и Python для анализа данных	NaN	NaN	67000.0	NaN	NaN	4.6	NaN	NaN	NaN	Coursera	NaN	NaN
3685	3686	Hacia una práctica constructivista en el aula	NaN	NaN	62000.0	All	NaN	4.9	NaN	NaN	NaN	Coursera	NaN	NaN
3686	3687	A Crash Course in Data Science	NaN	NaN	130000.0	NaN	NaN	NaN	NaN	NaN	NaN	Coursera	NaN	NaN

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Math Symbol

Close Punctuation

Open Punctuation

Dash Punctuation

Space Separator

Letter Number

Other Symbol

Modifier Symbol

Final Punctuation

Modifier Letter

Control

Most occurring scripts

Most frequent character per script

Han

Latin

Common

Katakana

Hiragana

Cyrillic

Arabic

Hangul

Most occurring blocks

Most frequent character per block

ASCII

None

Arabic

Cyrillic

Punctuation

Katakana

Hiragana

CJK

Letterlike Symbols

Number Forms

Math Operators

Hangul

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Other Punctuation

Most occurring scripts